no code implementations • 14 Mar 2024 • Ellie Prosser, Matthew Edwards
Powerful generative Large Language Models (LLMs) are becoming popular tools amongst the general public as question-answering systems, and are being utilised by vulnerable groups such as children.
1 code implementation • 22 Oct 2023 • Alexander P. Welsh, Matthew Edwards
Security classifiers, designed to detect malicious content in computer systems and communications, can underperform when provided with insufficient training data.
no code implementations • 9 Mar 2022 • Kathryn A. McGurk, Sean L. Zheng, Albert Henry, Katherine Josephs, Matthew Edwards, Antonio de Marvao, Nicola Whiffin, Angharad Roberts, Thomas R. Lumbers, Declan P. O Regan, James S. Ware
We were interested to read the recent update on recommendations for reporting of secondary findings in clinical sequencing1, and the accompanying updated list of genes in which secondary findings should be sought (ACMG SF v3. 0)2.
no code implementations • 17 Aug 2019 • Bilwaj Gaonkar, Joel Beckett, Mark Attiah, Christine Ahn, Matthew Edwards, Bayard Wilson, Azim Laiwalla, Banafsheh Salehi, Bryan Yoo, Alex Bui, Luke Macyszyn
Translation of fully automated deep learning based medical image segmentation technologies to clinical workflows face two main algorithmic challenges.
no code implementations • 3 Oct 2018 • Bilwaj Gaonkar, Matthew Edwards, Alex Bui, Matthew Brown, Luke Macyszyn
In the extreme, we observed that a model trained on patches extracted from just one scan, with each patch augmented 50 times; achieved a Dice score of 0. 73 in a validation set of 40 cases.